Multiscale spectral imaging for food safety with relevance to dairy processing

Multi-modal spectral imaging (e.g., Raman scattering, reflectance, darkfield) in different wavelength ranges (such as Infrared, near Infrared, Visible) at different spatial scales (from microscopic to macroscopic), combined with chemometrics approaches were investigated for the quality control of dairy products and define the hygienic level of the surfaces related to the dairy industry. In order to achieve these goals, the thesis has been developed in three steps: Quality control: identification of different dairy products based on macronutrients and lactose quantification in whole milk. Food safety: detection of dairy contamination on metallic surfaces related to food processing Food safety: detection of bacteria and biofilm contamination on surfaces related to the dairy industry. In addition, the detection of biofilm growth in presence of milk simulating possible scenarios in dairy processing has been carried out

Hydration of hydrogels studied by near-infrared hyperspectral imaging

Hydrogels are an important class of biomaterials that can absorb large quantities of water. In this study, changes in hydration of natural hydrogels (agar, chitosan, gelatin, starch, and blends of each with chitosan) during storage and rehydration were studied by using near-infrared hyperspectral imaging (NIR-HSI). Moisture content was calculated based on changes in sample weight during hydration. The NIR-HSI data were acquired by using a push-broom system operating in diffuse reflectance in the wavelength range 943 to 1650 nm. A novel synthesis method was developed to enable common preparation of each hydrogel. Mean spectra obtained from the hyperspectral images were analyzed, and predictive models for moisture content were developed by using partial least squares regression. Models were compared in predictive performance by using an independent validation set of data. The optimal model in predictive performance was a 1 latent variable partial least squares regression model developed on second derivative and mean centered pseudo-absorbance data in the wavelength range 943 to 1272 nm. This model was applied to pixel spectra from samples in the validation set to inspect spatial variations during dehydration and rehydration. Challenges associated with NIR-HSI of hydrogels with a large variation in moisture content are discussed

Multi-omics analysis reveals attenuation of cellular stress by empagliflozin in high glucose-treated human cardiomyocytes

Abstract Background Sodium–glucose cotransporter 2 (SGLT2) inhibitors constitute the gold standard treatment for type 2 diabetes mellitus (T2DM). Among them, empagliflozin (EMPA) has shown beneficial effects against heart failure. Because cardiovascular diseases (mainly diabetic cardiomyopathy) are the leading cause of death in diabetic patients, the use of EMPA could be, simultaneously, cardioprotective and antidiabetic, reducing the risk of death from cardiovascular causes and decreasing the risk of hospitalization for heart failure in T2DM patients. Interestingly, recent studies have shown that EMPA has positive benefits for people with and without diabetes. This finding broadens the scope of EMPA function beyond glucose regulation alone to include a more intricate metabolic process that is, in part, still unknown. Similarly, this significantly increases the number of people with heart diseases who may be eligible for EMPA treatment. Methods This study aimed to clarify the metabolic effect of EMPA on the human myocardial cell model by using orthogonal metabolomics, lipidomics, and proteomics approaches. The untargeted and multivariate analysis mimicked the fasting blood sugar level of T2DM patients (hyperglycemia: HG) and in the average blood sugar range (normal glucose: NG), with and without the addition of EMPA. Results Results highlighted that EMPA was able to modulate and partially restore the levels of multiple metabolites associated with cellular stress, which were dysregulated in the HG conditions, such as nicotinamide mononucleotide, glucose-6-phosphate, lactic acid, FA 22:6 as well as nucleotide sugars and purine/pyrimidines. Additionally, EMPA regulated the levels of several lipid sub-classes, in particular dihydroceramide and triacylglycerols, which tend to accumulate in HG conditions resulting in lipotoxicity. Finally, EMPA counteracted the dysregulation of endoplasmic reticulum-derived proteins involved in cellular stress management. Conclusions These results could suggest an effect of EMPA on different metabolic routes, tending to rescue cardiomyocyte metabolic status towards a healthy phenotype. Graphical Abstrac

Geographical characterization by MAE-HPLC and NIR methodologies and carbonic anhydrase inhibition of Saffron components

A microwave-assisted extraction method was optimised for the recovery of bioactive compounds from Crocus sativus L. stigmas with the use of water/ethanol mixture. HPLC-DAD was employed to evaluate the extraction parameters, in particular, solvent type and volume, and the duration of the procedure. Microwave-assisted extraction enhanced the recovery of the active principles, limiting extraction time and solvent waste. Moreover, NIR experiments were performed in order to compare spectra in pseudo-absorbance of Saffron samples with different geographical origins through the application of the chemometric techniques. Moreover, the biological evaluation of crocin 1, safranal and its semisynthetic derivatives as selective inhibitors of five isoforms of human carbonic anhydrase was also explored. (C) 2016 Elsevier Ltd. All rights reserved

MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues

Salivary gland tumors are relatively uncommon neoplasms that represent less than 5% of head and neck tumors, and about 90% are in the parotid gland. The wide variety of histologies and tumor characteristics makes diagnosis and treatment challenging. In the present study, Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to discriminate the pathological regions of patient-derived biopsies of parotid neoplasms by metabolomic and lipidomic profiles. Fresh frozen parotid tissues were analyzed by MALDI time-of-flight (TOF) MSI, both in positive and negative ionization modes, and additional MALDI-Fourier-transform ion cyclotron resonance (FT-ICR) MSI was carried out for metabolite annotation. MALDI-TOF-MSI spatial segmentation maps with different molecular signatures were compared with the histologic annotation. To maximize the information related to specific alterations between the pathological and healthy tissues, unsupervised (principal component analysis, PCA) and supervised (partial least squares-discriminant analysis, PLS-DA) multivariate analyses were performed presenting a 95.00% accuracy in cross-validation. Glycerophospholipids significantly increased in tumor tissues, while sphingomyelins and triacylglycerols, key players in the signaling pathway and energy production, were sensibly reduced. In addition, a significant increase of amino acids and nucleotide intermediates, consistent with the bioenergetics request of tumor cells, was observed. These results underline the potential of MALDI-MSI as a complementary diagnostic tool to improve the specificity of diagnosis and monitoring of pharmacological therapies

Integrated plasma metabolomics and lipidomics profiling highlights distinctive signature of hepatocellular carcinoma in HCV patients

Abstract Background Early diagnosis of hepatocellular carcinoma (HCC) is essential towards the improvement of prognosis and patient survival. Circulating markers such as α-fetoprotein (AFP) and micro-RNAs represent useful tools but still have limitations. Identifying new markers can be fundamental to improve both diagnosis and prognosis. In this approach, we harness the potential of metabolomics and lipidomics to uncover potential signatures of HCC. Methods A combined untargeted metabolomics and lipidomics plasma profiling of 102 HCV-positive patients was performed by HILIC and RP-UHPLC coupled to Mass Spectrometry. Biochemical parameters of liver function (AST, ALT, GGT) and liver cancer biomarkers (AFP, CA19.9 e CEA) were evaluated by standard assays. Results HCC was characterized by an elevation of short and long-chain acylcarnitines, asymmetric dimethylarginine, methylguanine, isoleucylproline and a global reduction of lysophosphatidylcholines. A supervised PLS-DA model showed that the predictive accuracy for HCC class of metabolomics and lipidomics was superior to AFP for the test set (100.00% and 94.40% vs 55.00%). Additionally, the model was applied to HCC patients with AFP values < 20 ng/mL, and, by using only the top 20 variables selected by VIP scores achieved an Area Under Curve (AUC) performance of 0.94. Conclusion These exploratory findings highlight how metabo-lipidomics enables the distinction of HCC from chronic HCV conditions. The identified biomarkers have high diagnostic potential and could represent a viable tool to support and assist in HCC diagnosis, including AFP-negative patients. Graphical abstrac

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy)

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches